npj Computational Materials最新文献

{"title":"NeuralMag: an open-source nodal finite-difference code for inverse micromagnetics","authors":"C. Abert, F. Bruckner, A. Voronov, M. Lang, S. A. Pathak, S. Holt, R. Kraft, R. Allayarov, P. Flauger, S. Koraltan, T. Schrefl, A. Chumak, H. Fangohr, D. Suess","doi":"10.1038/s41524-025-01688-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01688-1","url":null,"abstract":"<p>We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to perform efficient tensor operations on various parallel hardware, including CPUs, GPUs, and TPUs. The library implements a novel nodal finite-difference discretization scheme that provides improved accuracy over traditional finite-difference methods without increasing computational complexity. NeuralMag is particularly well-suited for solving inverse problems, especially those with time-dependent objectives, thanks to its automatic differentiation capabilities. Performance benchmarks show that NeuralMag is competitive with state-of-the-art simulation codes while offering enhanced flexibility through its Python interface and integration with high-level computational backends.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"608 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning","authors":"Ching-Chien Chen, Robert J. Appleton, Saswat Mishra, Kat Nykiel, Alejandro Strachan","doi":"10.1038/s41524-025-01682-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01682-7","url":null,"abstract":"<p>Pressure-induced phase transformations in materials are of interest in a range of fields, including geophysics, planetary sciences, and shock physics. In addition, the high-pressure phases can exhibit desirable properties, eliciting interest in materials science. Despite its importance, the process of finding new high-pressure phases, either experimentally or computationally, is time-consuming and often driven by intuition. In this study, we use graph neural networks trained on density functional theory (DFT) equation of state data of 2258 materials and 7255 phases to identify potential phase transitions. The model is used to explore possible phase transitions in 7677 pairs of phases and promising cases are confirmed or denied via DFT calculations. Importantly, the new data is added to the training set, the model is refined, and a new cycle of discovery is started. Within 13 iterations, we discovered 28 new high-pressure stable phases (never synthesized through high-pressure routes nor reported in high-pressure computational works) and rediscovered 18 pressure-induced phase transitions. The results provide new insight and classification of pressure-induced phase transitions in terms of the ambient properties of the phases involved.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"59 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy","authors":"Ganesh Narasimha, Dejia Kong, Paras Regmi, Rongying Jin, Zheng Gai, Rama Vasudevan, Maxim Ziatdinov","doi":"10.1038/s41524-025-01642-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01642-1","url":null,"abstract":"<p>Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn₂As₂, a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"13 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144320082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polymer design for solvent separations by integrating simulations, experiments and known physics via machine learning","authors":"Janhavi Nistane, Rohan Datta, Young Joo Lee, Harikrishna Sahu, Seung Soon Jang, Ryan Lively, Rampi Ramprasad","doi":"10.1038/s41524-025-01681-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01681-8","url":null,"abstract":"<p>This study guides the discovery of sustainable high-performance polymer membranes for organic binary solvent separations. We focus on solvent diffusivity in polymers, a key factor in quantifying solvent transport. Traditional experimental and computational methods for determining diffusivity are time- and resource-intensive, while current machine learning (ML) models often lack accuracy outside their training domains. To overcome this, we fuse experimental and simulated diffusivity data to train physics-enforced multi-task ML models, achieving more robust predictions in unseen chemical spaces and outperforming single-task models in data-limited scenarios. Next, we address the challenge of identifying optimal membranes for a model toluene-heptane separation, identifying polyvinyl chloride (PVC) as the optimal membrane among 13,000 polymers, consistent with literature findings, thereby validating our methodology. Expanding our search, we screen 1 million publicly available and 7 million chemically recyclable polymers, identifying greener halogen-free alternatives to PVC. This capability is expected to advance membrane design for solvent separations.</p><figure></figure>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"240 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144320008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inverse mapping of properties to composition through generative modeling for designing molten salts","authors":"Julian Barra, Rajni Chahal, Shubhojit Banerjee, Massimiliano Lupo Pasini, Stephan Irle, Stephen Lam","doi":"10.1038/s41524-025-01638-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01638-x","url":null,"abstract":"<p>Generative modeling (GM) has been increasingly used for the inverse design and optimization of materials, yet its application to molten salt mixtures remains unexplored despite how a successful approach to the inverse design of molten salts would contribute to efficiently exploiting their customizability and unlocking their advantages in applications, such as energy production and energy storage. This work presents a workflow for the inverse design of molten salts with targeted density values, addressing the challenge of representing these complex mixtures in GM. A dataset of critically evaluated molten salt densities is used to train a variational autoencoder coupled with a predictive deep neural network, which then can be used to generate new molten salt compositions with desired density values. The effectiveness of the approach is demonstrated by designing mixtures with distinct densities and validating the predicted values using ab initio molecular dynamics simulations.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"40 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LLM-Prop: predicting the properties of crystalline materials using large language models","authors":"Andre Niyongabo Rubungo, Craig Arnold, Barry P. Rand, Adji Bousso Dieng","doi":"10.1038/s41524-025-01536-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01536-2","url":null,"abstract":"<p>The prediction of crystal properties plays a crucial role in materials science and applications. Current methods for predicting crystal properties focus on modeling crystal structures using graph neural networks (GNNs). However, accurately modeling the complex interactions between atoms and molecules within a crystal remains a challenge. Surprisingly, predicting crystal properties from crystal text descriptions is understudied, despite the rich information and expressiveness that text data offer. In this paper, we develop and make public a benchmark dataset (TextEdge) that contains crystal text descriptions with their properties. We then propose LLM-Prop, a method that leverages the general-purpose learning capabilities of large language models (LLMs) to predict properties of crystals from their text descriptions. LLM-Prop outperforms the current state-of-the-art GNN-based methods by approximately 8% on predicting band gap, 3% on classifying whether the band gap is direct or indirect, and 65% on predicting unit cell volume, and yields comparable performance on predicting formation energy per atom, energy per atom, and energy above hull. LLM-Prop also outperforms the fine-tuned MatBERT, a domain-specific pre-trained BERT model, despite having 3 times fewer parameters. We further fine-tune the LLM-Prop model directly on CIF files and condensed structure information generated by Robocrystallographer and found that LLM-Prop fine-tuned on text descriptions provides a better performance on average. Our empirical results highlight the importance of having a natural language input to LLMs to accurately predict crystal properties and the current inability of GNNs to capture information pertaining to space group symmetry and Wyckoff sites for accurate crystal property prediction.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"12 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144311887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Creation and control of valley currents in graphene by few cycle light pulses","authors":"Deepika Gill, Sangeeta Sharma, Kay Dewhurst, Sam Shallcross","doi":"10.1038/s41524-025-01689-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01689-0","url":null,"abstract":"<p>Well established for the visible spectrum gaps of the transition metal dichalcogenide family, valleytronics—the control of valley charge and current by light—is comparatively unexplored for the THz gaps that characterize graphene and topological insulators. Here we show that few cycle pulses of THz light can create and control a &gt;90% valley polarized current in graphene, with lightwave control over the current magnitude and direction. This is underpinned by a light-matter symmetry breaking in the ultrafast limit of circularly polarized light, characterized by a symmetry lowering of the excited state charge distribution. Our findings both highlight the richness of few cycle light pulses in control over quantum matter, and provide a route towards a “THz valleytronics” in meV gapped systems.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"21 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144311941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Massive discovery of crystal structures across dimensionalities by leveraging vector quantization","authors":"ZiJie Qiu, Luozhijie Jin, Zijian Du, Hongyu Chen, Guanyao Mao, Yan Cen, Siqi Sun, Yongfeng Mei, Hao Zhang","doi":"10.1038/s41524-025-01613-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01613-6","url":null,"abstract":"<p>Discovering new functional crystalline materials through computational methods remains a challenge in materials science. We introduce VQCrystal, a deep learning framework leveraging discrete latent representations to overcome key limitations to crystal generation and inverse design. VQCrystal employs a hierarchical VQ-VAE architecture to encode global and atom-level crystal features, coupled with an inter-atomic potential model and a genetic algorithm to realize property-targeted inverse design. Benchmark evaluations on diverse datasets demonstrate VQCrystal’s capabilities in representation learning and crystal discovery. We further apply VQCrystal for both 3D and 2D material design. For 3D materials, the density-functional theory validation confirmed that 62.22% of bandgaps and 99% of formation energies of the 56 filtered materials matched the target range. 437 generated materials were validated as existing entries in the full MP-20 database outside the training set. For 2D materials, 73.91% of 23 filtered structures exhibited high stability with formation energies below -1 eV/atom.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"18 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144305354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"First-principles Hubbard parameters with automated and reproducible workflows","authors":"Lorenzo Bastonero, Cristiano Malica, Eric Macke, Marnik Bercx, Sebastiaan Huber, Iurii Timrov, Nicola Marzari","doi":"10.1038/s41524-025-01685-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01685-4","url":null,"abstract":"<p>We introduce an automated, flexible framework (aiida-hubbard) to self-consistently calculate Hubbard <i>U</i> and <i>V</i> parameters from first-principles. By leveraging density-functional perturbation theory, the computation of the Hubbard parameters is efficiently parallelized using multiple concurrent and inexpensive primitive cell calculations. Furthermore, the intersite <i>V</i> parameters are defined on-the-fly during the iterative procedure to account for atomic relaxations and diverse coordination environments. We devise a novel, code-agnostic data structure to store Hubbard related information together with the atomistic structure, to enhance the reproducibility of Hubbard-corrected calculations. We demonstrate the scalability and reliability of the framework by computing in high-throughput fashion the self-consistent onsite <i>U</i> and intersite <i>V</i> parameters for 115 Li-containing bulk solids with up to 32 atoms in the unit cell. Our analysis of the Hubbard parameters calculated reveals a significant correlation of the onsite <i>U</i> values on the oxidation state and coordination environment of the atom on which the Hubbard manifold is centered, while intersite <i>V</i> values exhibit a general decay with increasing interatomic distance. We find, e.g., that the numerical values of <i>U</i> for the 3d orbitals of Fe and Mn can vary up to 3 eV and 6 eV, respectively; their distribution is characterized by typical shifts of about 0.5 eV and 1.0 eV upon change in oxidation state, or local coordination environment. For the intersite <i>V</i> a narrower spread is found, with values ranging between 0.2 eV and 1.6 eV when considering transition metal and oxygen interactions. This framework paves the way for the exploration of redox materials chemistry and high-throughput screening of <i>d</i> and <i>f</i> compounds across diverse research areas, including the discovery and design of novel energy storage materials, as well as other technologically-relevant applications.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"25 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144305389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An informatics framework for the design of sustainable, chemically recyclable, synthetically accessible, and durable polymers","authors":"Joseph Kern, Yong-Liang Su, Will Gutekunst, Rampi Ramprasad","doi":"10.1038/s41524-025-01683-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01683-6","url":null,"abstract":"<p>We present a novel approach to designing durable and chemically recyclable ring-opening polymerization (ROP) class polymers. This approach employs digital reactions using virtual forward synthesis (VFS) to generate over 7 million ROP polymers and machine learning techniques to rapidly predict thermal, thermodynamic, and mechanical properties crucial for performance and recyclability. This methodology enables the generation and evaluation of millions of hypothetical ROP polymers from known and commercially available molecules, guiding the selection of approximately 35,000 candidates with optimal features for sustainability and utility. Three of these recommended candidates have passed validation tests in the physical lab — two of the three by others, as published previously elsewhere, and one of them is a new thiocane polymer synthesized, tested, and reported here. This paper highlights the potential of VFS and machine learning to enable a large-scale search of the polymer universe and advance the development of recyclable and environmentally benign polymers.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"117 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}